def prepare_data(self, images, labels=None):
if (labels is None):
for (i, image) in enumerate(images):
number = ("%0.3d" % (i+1))
path_save = path.join(self.dn_test_out, mkdir=True)
image, _ = dip.preprocessor(image, None)
original_name = (const.fn_PREPROCESSING % (number))
data.imwrite(path.join(path_save, original_name), image)
yield self.arch.prepare_input(image)
else:
for (image, label) in zip(images, labels):
(image, label) = dip.preprocessor(image, label)
yield self.arch.prepare_input(image), self.arch.prepare_input(label)
def tolabel():
dn_tolabel = path.out(const.dn_TOLABEL, mkdir=False)
if path.exist(dn_tolabel):
dir_save = path.out(const.dn_TOLABEL)
images = data.fetch_from_path(dir_save)
for (i, image) in enumerate(images):
path_save = path.join(dir_save, "label", mkdir=True)
file_name = ("%0.3d.png" % (i+1))
file_save = path.join(path_save, file_name)
img_pp, _ = dip.preprocessor(image, None)
data.imwrite(file_save, img_pp)
else:
print("\n>> Folder not found (%s)\n" % dn_tolabel)
def save_predict(self, original, image):
path_save = path.join(self.dn_test_out, mkdir=True)
with open(path.join(path_save, (const.fn_SEGMENTATION)), 'w+') as f:
for (i, image) in enumerate(image):
number = ("%0.3d" % (i+1))
image_name = (const.fn_PREDICT % (number))
image = dip.posprocessor(original[i], self.arch.prepare_output(image))
data.imwrite(path.join(path_save, image_name), image)
seg = (image == 255).sum()
f.write(("Image %s was approximately %f segmented (%s pixels)\n" % (number, (seg/image.size), seg)))
original_name = (const.fn_ORIGINAL % (number))
data.imwrite(path.join(path_save, original_name), original[i])
overlay_name = (const.fn_OVERLAY % (number))
overlay = dip.overlay(original[i], image)
data.imwrite(path.join(path_save, overlay_name), overlay)
f.close()